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Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms
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Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms
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Journal Article
Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms
2013
Overview
In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the CS algorithm and ABC algorithm-based denoising approach give better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) as compared to PSO-based denoising approach. The proposed technique has been tested on satellite images. The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state-of-the-art image denoising techniques.
Publisher
The Institution of Engineering and Technology,Institution of Engineering and Technology,John Wiley & Sons, Inc
Subject
/ artificial bee colony technique
/ Detection, estimation, filtering, equalization, prediction
/ Exact sciences and technology
/ improved subband adaptive thresholding function
/ Information, signal and communications theory
/ Signal and communications theory
